Difference between revisions of "MIR workshop 2008 notes"
From CCRMA Wiki
| Line 5: | Line 5: | ||
= Timing and Segmentation = | = Timing and Segmentation = | ||
| − | + | == Onset Detection == | |
==== Papers ==== | ==== Papers ==== | ||
==== Code ==== | ==== Code ==== | ||
| − | + | == Beat Extraction == | |
==== Papers ==== | ==== Papers ==== | ||
==== Code ==== | ==== Code ==== | ||
| − | + | == Tempo Extraction == | |
==== Papers ==== | ==== Papers ==== | ||
==== Code ==== | ==== Code ==== | ||
| − | + | = Feature Extraction = | |
== Low Level Features == | == Low Level Features == | ||
=== Zero Crossing, Temporal centroid, Log Attack time, Attack slope), Spectral features (Centroid, Flux, RMS, Rolloff, Flatness, Kurtosis, Brightness),Spectral bands, Log spectrogram === | === Zero Crossing, Temporal centroid, Log Attack time, Attack slope), Spectral features (Centroid, Flux, RMS, Rolloff, Flatness, Kurtosis, Brightness),Spectral bands, Log spectrogram === | ||
| Line 28: | Line 28: | ||
=== "Fingerprints" === | === "Fingerprints" === | ||
| − | + | = Analysis / Decision Making = | |
== Classification == | == Classification == | ||
=== Heuristic Analysis === | === Heuristic Analysis === | ||
| Line 37: | Line 37: | ||
=== Density distance measures (centroid distance, EMD, KL-divergence, etc) === | === Density distance measures (centroid distance, EMD, KL-divergence, etc) === | ||
=== k-Means === | === k-Means === | ||
| − | + | == Clustering == | |
=== GMM === | === GMM === | ||
=== HMM === | === HMM === | ||
== Nested classifier / Anchor-space / template-based systems == | == Nested classifier / Anchor-space / template-based systems == | ||
| − | + | = Model / Data Preparation Techniques = | |
== Data Preparation == | == Data Preparation == | ||
=== PCA / LDA === | === PCA / LDA === | ||
| Line 48: | Line 48: | ||
=== Model organization === | === Model organization === | ||
* concept, design, data set construction and organization | * concept, design, data set construction and organization | ||
| + | |||
| + | = Evaluation Methodology = | ||
| + | == Feature selection == | ||
| + | == Cross Validation == | ||
| + | == Information Retrieval metrics (precision, recall, F-Measure) == | ||
Revision as of 10:25, 1 August 2008
This page is intended to supplement the lecture material found in the class - providing extra tutorials, support, references for further reading, or demonstration code snippets for those interested in a given topic. Please contribute to this growing list of resources. Do you have a great explanation of how a technique works? Found a great Java applet that illustrates a concept? Discovered a great survey of the field for a particular area? Please add it for the benefit of future students. Thanks!
I encourage you to ADD links and sections - but please do not REMOVE headings or items from the page.
Contents
- 1 Timing and Segmentation
- 2 Feature Extraction
- 3 Analysis / Decision Making
- 4 Model / Data Preparation Techniques
- 5 Evaluation Methodology
Timing and Segmentation
Onset Detection
Papers
Code
Beat Extraction
Papers
Code
Tempo Extraction
Papers
Code
Feature Extraction
Low Level Features
Zero Crossing, Temporal centroid, Log Attack time, Attack slope), Spectral features (Centroid, Flux, RMS, Rolloff, Flatness, Kurtosis, Brightness),Spectral bands, Log spectrogram
Chroma bins
MFCC
MPEG-7
Higher-level features
Key Estimation
Chord Estimation
Genre (genre, artist ID, similarity)
"Fingerprints"
Analysis / Decision Making
Classification
Heuristic Analysis
Distance measures (Euclidean, Manhattan, etc.)
k-NN
SVM / One-class SVM
Clustering and probability density models
Density distance measures (centroid distance, EMD, KL-divergence, etc)
k-Means
Clustering
GMM
HMM
Nested classifier / Anchor-space / template-based systems
Model / Data Preparation Techniques
Data Preparation
PCA / LDA
Scaling data
Model organization
- concept, design, data set construction and organization
